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In contemporary wildlife conservation, drones have become essential for the non-invasive monitoring of animal populations and habitats. However, the sensitive data captured by drones, including images and videos, require robust encryption to prevent unauthorized access and exploitation. This paper presents a novel encryption algorithm designed specifically for safeguarding wildlife data. The proposed approach integrates one-dimensional and two-dimensional memory cellular automata (1D MCA and 2D MCA) with a bitwise XOR operation as an intermediate confusion layer. The 2D MCA, guided by chaotic rules from the sine-exponential (SE) map, utilizes varying neighbor configurations to enhance both diffusion and confusion, making the encryption more resilient to attacks. A final layer of 1D MCA, controlled by pseudo-random number generators, ensures comprehensive diffusion and confusion across the image. The SHA-256 hash of the input image is used to derive encryption parameters, providing resistance against plaintext attacks. Extensive performance evaluations demonstrate the effectiveness of the proposed scheme, which balances security and complexity while outperforming existing algorithms.

In this paper, we report an effective cryptosystem aimed at securing the transmission of medical images in an Internet of Healthcare Things (IoHT) environment. This contribution investigates the dynamics of a 2-D trigonometric map designed using some well-known maps: Logistic-sine-cosine maps. Stability analysis reveals that the map has an infinite number of solutions. Lyapunov exponent, bifurcation diagram, and phase portrait are used to demonstrate the complex dynamic of the map. The sequences of the map are utilized to construct a robust cryptosystem. First, three sets of key streams are generated from the newly designed trigonometric map and are used jointly with the image components (R, G, B) for hamming distance calculation. The output distance-vector, corresponding to each component, is then Bit-XORed with each of the key streams. The output is saved for further processing. The decomposed components are again Bit-XORed with key streams to produce an output, which is then fed into the conditional shift algorithm. The Mandelbrot Set is used as the input to the conditional shift algorithm so that the algorithm efficiently applies confusion operation (complete shuffling of pixels). The resultant shuffled vectors are then Bit-XORed (Diffusion) with the saved outputs from the early stage, and eventually, the image vectors are combined to produce the encrypted image. Performance analyses of the proposed cryptosystem indicate high security and can be effectively incorporated in an IoHT framework for secure medical image transmission.

In this paper, we propose a new chaos-based encryption scheme for medical images. It is based on a combination of chaos and DNA computing under the scenario of two encryption rounds, preceded by a key generation layer, and follows the permutation-substitution-diffusion structure. The SHA-256 hash function alongside the initial secret keys is employed to produce the secret keys of the chaotic systems. Each round of the proposed algorithm involves six steps, i.e., block-based permutation, pixel-based substitution, DNA encoding, bit-level substitution (i.e., DNA complementing), DNA decoding, and bit-level diffusion. A thorough search of the relevant literature yielded only this time the pixel-based substitution and the bit-level substitution are used in cascade for image encryption. The key-streams in the bit-level substitution are based on the logistic-Chebyshev map, while the sine-Chebyshev map allows producing the key-streams in the bit-level diffusion. The final encrypted image is obtained by repeating once the previous steps using new secret keys. Security analyses and computer simulations both confirm that the proposed scheme is robust enough against all kinds of attacks. Its low complexity indicates its high potential for real-time and secure image applications.

This paper presents a fast and efficient cryptosystem for enciphering digital images. It employs two of the most prominent dynamical systems-chaotic maps and cellular automata. The key streams in the proposed encryption scheme are derived from the SHA-256 hash function. Hash functions produce the digest of the input plaintext, known as a hash value, which can be considered as a unique signature of the input. This makes the keys more plaintext dependent, which is a desirable property of a robust cryptosystem. These key streams are used as the secret keys (i.e., initial conditions and control parameters) of an improved one-dimensional (1-D) chaotic map, i.e., the Logistic-Sine map. As far as we know, this paper is a first that combines the well-known diffusion-confusion architecture and the fourth order 1-D memory cellular automata (MCA) for image encryption. First, a pixel-wise XOR operation is applied to the original image, followed by a pixel-wise random permutation. The resulting image is decomposed into four blocks according to the quadtree decomposition strategy. Then, a fourth order reversible MCA is applied, the blocks obtained from the quadtree decomposition are considered as the initial MCA configurations, and the transition rules are determined using the chaotic map. The performance analyses show that the proposed encryption scheme presents a high immunity against all kind of attacks while maintaining a low complexity, which outcome a notably better performance/complexity trade-off compared to some recently proposed image schemes.

The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.

This paper presents an improved Teaching-Learning-Based Optimization (TLBO) for optimal tuning of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controllers. The original TLBO is characterized by easy implementation and is mainly free of control parameters. Unfortunately, TLBO may suffer from population diversity losses in some cases, leading to local optimum and premature convergence. In this study, three approaches are considered for improving the original TLBO (i) randomness improvement, (ii) three new mutation strategies (iii) hyperchaotic perturbation strategy. In the first approach, all random numbers in the original TLBO are substituted by the hyperchaotic map sequence to boost exploration capability. In the second approach, three mutations are carried out to explore a new promising search space. The obtained solution is further improved in the third strategy by implementing a new perturbation equation. The proposed HTLBO was evaluated with 26 test functions. The obtained results show that HTLBO outperforms the TBLO algorithm and some state-of-the-art algorithms in robustness and accuracy in almost all experiments. Moreover, the efficacy of the proposed HTLBO is justified by involving it in the power system stability problem. The results consist of the Integral of Absolute Error (ITAE) and eigenvalue analysis of electromechanical modes demonstrate the superiority and the potential of the proposed HTLBO based PSSs and SVC controllers over a wide range of operating conditions. Besides, the advantage of the proposed coordination design controllers was confirmed by comparing them to PSSs and SVC tuned individually.

Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.